ComputerAided_Design_Engineering_amp_Manufactur.pdf

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FIGURE 1.8 System Architecture and Case Study System Architecture The process plan for the part is developed on the basis of the geometrical and technological information of the raw stock and the workpiece that is available from a solid-modeler-based CAD data model. This necessitates the integration of the process planner with a feature-based design system (FBDS) where the necessary part geometry can be created in terms of its manufacturing features, and the appropriate technological information can be specified on the component geometry. With this objective in mind, the blackboard-based process planning system (BBPP) has been integrated with a FBDS such as the CAEDS package (Figure 1.8). The process planner derives all of its information (i.e., required for process planning) © 2001 by CRC Press LLC TABLE 1.2 Part Product Data Model Type Raw-Stock Template Raw-stock type (bars slabs, etc.) Material Raw-stock material Raw-size Raw-stock dimensions Cut-size Finished workpiece dimensions Finished-piece-hardness Hardness of finished workpiece Raw-stock-hardness Hardness of raw stock Feature-id A Feature Template in the Finished Part Index number of feature Feature-surface Index number of surfaces of the feature Feature-type Type of feature Dimension Dimensions of the feature Location Location of the feature Attachment-faces Index number of surfaces that are adjacent to the surfaces of the feature Tolerance-info Tolerance information associated with the feature Finish Desired surface finish quality for the surfaces of the feature System architecture of the BBPP system with the FBDS system.
from the FBDS. The user also specifies various technological information attributes such as surface finish and hardness specifications of the part in addition to the task of creating the part geometry. The technological information attributes are written to an output file while the geometric information of the part is written to a universal file formatted CAD output file. 18 The CAD output file is interpreted by an interface program and all relevant geometric and technological information associated with the features is translated to the product data model of the part. The process planner interprets the PDM and develops the process plans for the part. The process plan is then written in a suitable format for use in manufacturing. Subsequently, the process plan information is used for generating the NC tool path for machining the features on an NC machine tool, utilizing the services of a dedicated NC tool path planning module. Blackboard-Based Process Planner The blackboard (BB) architecture-based process planning framework has been implemented in a prototype expert system BBPP, using the CLIPS expert system shell. (The current capabilities of the BBPP system are summarized in Table 1.3.) The domain expertise of the various participating experts (knowledge sources in BB terminology) that have been incorporated in the BBPP system is shown in Table 1.4. The control expert (control component in BB terminology) consists of a set of rules that performs the following functions (Figure 1.9): (a) correlating and interpreting data provided by different experts, (b) directing the solution generation process between two subsequent process planning phases, (c) activating the participating experts on the basis of the current state of the BB data base, and (d) directing output from the BB in each one of the process planning phases to the appropriate sections of the route sheet. The rules for the control expert have been developed on the basis of some heuristic control algorithms. In the BBPP system, the process planning task is partitioned into five separate phases that correspond to certain activities being performed during process planning. The BB data base is a data structure in which slots are provided for the specification, retrieval, and modification of different types of data pertaining to the different segments of the process plan (Table 1.5). The different participating experts refer to the BB data base for the data required to perform their tasks © 2001 by CRC Press LLC TABLE 1.3 Current Capabilities of BBPP System Process Plan Feature Capability <strong>Manufactur</strong>ing features Slots, Pockets, Protrusions, and Holes Machining process Drilling, Boring, Shaping, Planning, Slab Milling, End Milling, and Sawing Finishing operations Reaming and Grinding Heat treatment operations Quenching, Carbonitriding, Cyaniding, and Carburizing TABLE 1.4 Domain Expertise of Different Experts in the BBPP System Name of Expert Fields of Expertise in the Process Plan Domain Machining Machining strategies Machine-tools Machine-tool capabilities Cutting-tools Cutting-tool information Material Material behavior Cost Machining and manufacturing cost Surface finish Finishing operations Heat treatment Heat treatment operations Machinability Machining parameters Setup Machining setups Sequence planning Setup sequencing
Page 1 and 2: COMPUTER-AIDED DESIGN, ENGINEERING,
Page 3 and 4: Library of Congress Cataloging-in-P
Page 5 and 6: Editor Cornelius T. Leondes, B.S.,
Page 7 and 8: Chapter 1 Chapter 2 Chapter 3 Chapt
Page 9 and 10: the implementation of the IPD syste
Page 11 and 12: In our IPD system implementations,
Page 13 and 14: 2. Specification of analysis-specif
Page 15 and 16: FIGURE 1.2 base or the design insta
Page 17 and 18: FIGURE 1.4 System architecture of t
Page 19 and 20: of a beam is considered for FEA. Th
Page 21 and 22: FIGURE 1.7 through the control expe
Page 23: 2. machining facility (e.g., gang m
Page 27 and 28: Depending on the type of feature to
Page 29 and 30: TABLE 1.6 Tolerance synthesis is de
Page 31 and 32: FIGURE 1.12 Display of the NC tool
Page 33 and 34: component geometric entities and va
Page 35 and 36: FIGURE 1.14 The system architecture
Page 37 and 38: TABLE 1.10 User Input for the Toler
Page 39 and 40: TABLE 1.11 Tolerance Specifications
Page 41 and 42: 7. Z. Young and I. R. Groose. A rul
Page 43 and 44: FIGURE 2.1 Tool paths generated for
Page 45 and 46: FIGURE 2.2 sented by instances of f
Page 47 and 48: TABLE 2.1 A CAD-Generated Hole Conf
Page 49 and 50: FIGURE 2.6 mounted on the rotary ta
Page 51 and 52: FIGURE 2.8 fixture for the machinin
Page 53 and 54: FIGURE 2.10 or best-fit, or the cur
Page 55 and 56: FIGURE 2.11 Linear approximation of
Page 57 and 58: FIGURE 2.13 Circular approximation
Page 59 and 60: FIGURE 2.14(b) Circular approximati
Page 61 and 62: FIGURE 2.16 Types of biarcs. FIGURE
Page 63 and 64: FIGURE 2.18 Approximation of scanne
Page 65 and 66: FIGURE 2.20 A touch trigger probe c
Page 67 and 68: TABLE 2.4 Substitute Elements in CM
Page 69 and 70: FIGURE 2.21 CMM planning requiremen
Page 71 and 72: Product Model Representation for Co
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
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TABLE 2.7 Partial Listing of Dimens
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24. Makinouchi, S., Okamoto, M., an
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Bijan Shirinzadeh Monash University
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FIGURE 3.1 Trends in flexible manuf
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FIGURE 3.3 and constrain different
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Other Fixturing Techniques There ar
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FIGURE 3.6 contact point. The geome
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FIGURE 3.8 The vertical support fix
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and moments about the contact point
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een identified, the normals are use
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one of choosing the variables: such
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Fixture Module Location An importan
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FIGURE 3.15 Illustration of functio
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FIGURE 3.18 Minimum separation test
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direction) the face, respectively.
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FIGURE 3.21 Software structure for
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3.8 Conclusion A reconfigurable fix
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Heui Jae Pahk Seoul National Univer
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FIGURE 4.1(b) Conceptual framework
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4.3 Measurement Points Sampling and
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Surface S2 Measurement Path FIGURE
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FIGURE 4.4 Rough phase alignment ba
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deviation between the nominal CAD d
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FIGURE 4.6(a) Sum of squares distan
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FIGURE 4.7(c) Trailing edge. (c) th
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FIGURE 4.8(a) Profile tolerance of
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The calculated minimum form error i
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FIGURE 4.11(a) A typical mold havin
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FIGURE 4.11(d) Inspection results f
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FIGURE 4.12(c) Maximum deviation (p
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5.1 Introduction Developments in th
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process plan for a part (Figure 5.3
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FIGURE 5.5 leading to the developme
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the previously stored process plan
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FIGURE 5.7 Techniques of defining a
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Feature Recognition and Extraction
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in relation to another feature (e.g
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FIGURE 5.9 Framework for building a
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FIGURE 5.10 Process plan content.
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FIGURE 5.12 Schematic sketch of the
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Several techniques of defining a fe
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TABLE 5.1 Data Structure for Repres
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FIGURE 5.16 Classification of the t
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system to ensure that the part bein
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FIGURE 5.19 Graphical model of the
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TABLE 5.4 Data Structure for Repres
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FIGURE 5.20 Mapping between machini
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algorithms. (Some details of the pr
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FIGURE 5.24 An example rotational p
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FIGURE 5.26 Down_face-turn-up_face
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FIGURE 5.27 Procedure for machine t
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FIGURE 5.28 Determining the number
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DL is needed to be set only if the
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FIGURE 5.31 Operation sequencing co
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Cutting Tool Selection Selection of
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1. A tool is searched for in the da
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FIGURE 5.32 Inputs to optimization
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When these values are substituted,
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Usually, maximum and minimum speed
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FIGURE 5.33 Solution methodology.
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TABLE 5.6 Process Plan Internal Rep
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In a strict theoretical perspective
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18. Domazet, D. S. and Lu, S. C. Y,
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63. Prasad, A. V. S. R. K., Rao, P.
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CAD systems. The sample consists of
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learn to master the new system. An
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Although researchers appear to agre
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Link and Zmud28 found that organic
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Informal Training Informal CAD trai
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informal training programs, felt th
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6. C. A. Beatty, Tall Tales and Rea
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A.Y.C. Nee National University of S
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FIGURE 7.1 Planning, design, and ma
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A metal stamping can have the follo
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FIGURE 7.3 Strips used to notch out
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A Skeletal Approach for the Recogni
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These findings can be used to devel
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Semi-Direct Piloting In cases where
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a larger value, the die operations
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FIGURE 7.9 Symbolic relationship be
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FIGURE 7.11 The shape of the envelo
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TABLE 7.1 Schema for the Generation
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strong reasons to support a move to
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FIGURE 7.16 3-D CAD model of a part
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References Cheok, B.T. et al. (1994
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include the once forbidden generati
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A temporal matrix (T-Matrix) is pro
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FIGURE 8.1 A basic process. (From R
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FIGURE 8.4(a) Generate a new IG usi
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Note if the pair of nodes are both
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TABLE 8.1 Summary of the Rules Cond
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The correctness of these rules is e
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needs to show that after each full
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ule is violated, a warning should b
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Π1 Π3 Π4 FIGURE 8.8(a) After add
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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A ik � ‘U’ and is in a cycle
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FIGURE 8.12(a) Add [p2 t7 p7 t8 p4]
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FIGURE 8.12(c) Add a token to p8 vi
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FIGURE 8.12(e) Add [p8 t9 p14 t10 p
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FIGURE 8.13 An example of rule TT.0
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FIGURE 8.14 A GPN model of a machin
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FIGURE 8.15(b) The first exclusive
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FIGURE 8.15(d) The last exclusive T
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FIGURE 8.15(f) After entering the a
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FIGURE 8.15(h) Completion of Compon
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FIGURE 8.15(j) Two PP-generations:
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FIGURE 8.15(l) The last exclusive T
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t1 t5 p1 p5 t6 (a) t2 t3 t4 2 2 p2
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FIGURE 8.17 An example of partial f
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Theorem 10: If pi → pk in a synth
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Note that control transitions are o
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t j than the lower at , it indicate
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• Programming logic and VLSI arra
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7. Chao, D. Y. and D. T. Wang, Appl
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53. Villaroel, J. L., J. Martinez,
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q u : the numerator of the least ra
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geometric modeling, engineering ana
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In dimension driven or parametric d
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FIGURE 9.3 configuration of bodies
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FIGURE 9.6 FIGURE 9.7 Geometric Int
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FIGURE 9.8 Intersection of degrees
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will introduce a way to propagate p
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Completeness of Dimensions Complete
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FIGURE 9.12 3-D constraints. © 200
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The distance constraint from pt �
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therefore: Now merge the lists and
Page 329 and 330:
Quantity analysis methods have the
Page 331 and 332:
y a revolute joint. By selecting al
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